We compare different versions of our method (with loss configurations \(L^{\prime }_{mm}\)  and \(L_{cls}\)  respectively) to a baseline approach. Note that the baseline is a simple extension of Faster-RCNN [1]} and ConSE [2]}. We apply the inference strategy mentioned in Sec. REF  after first step training as we can still get a vector \(\mathbf {o} \in \mathbb {R}^{\mathrm {S}+1}\)  on the classification layer of Faster-RCNN network. We use two different architectures i.e., VGG-16 (V) [3]} and ResNet-50 (R) [4]} as the backbone of the Faster-RCNN during the first training step. In second step, we experiment with both Word2vec and GloVe as the semantic embedding vectors used to define \(\mathbf {W}_2\) . Fig. REF  illustrates some qualitative ZSD examples. More performance results of ZSD on other datasets is provided in the supplementary material.
